Introduction

In the previous chapter, it was explained why deep learning has become so popular nowadays, and PyTorch was introduced as one of the most popular libraries for developing deep learning solutions. Although the main syntax for building a neural network using PyTorch was explained, in this chapter, we will further explore the concept of neural networks.

Although neural network theory was developed several decades ago, since the concept evolved from the notion of the perceptron, different architectures have been created to solve different data problems in recent times. This is, in part, due to the different data formats that can be found in real-life data problems, such as text, audio, and images.

The purpose of this chapter is to pe into the topic of neural networks and their main advantages and disadvantages so that you can understand when and how to use them. Then, we will explain the building blocks of the most popular neural network architectures: artificial neural networks (ANNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs).

Following this, the process of building an effective model will be explained by solving a real-life regression problem. This includes preparing the data to be fed to the neural network (also known as data preprocessing), defining the neural network architecture to be used, and evaluating the performance of the model, with the objective of determining how it can be improved to achieve an optimal solution.

The aforementioned process will be done using one of the neural network architectures that will be discussed in this chapter, all while taking into consideration that the solution for each data problem should be carried out using the architecture that performs best for the data type in question. The other architectures will be used in subsequent chapters to solve more complicated data problems that involve using images and sequences of text as input data.

Note

All the code present in this chapter can be found at: https://packt.live/34MBauE.